Why the Modern-Day Corporation Should Consider a Data Estate

There’s a lot of truth in this assessment. Today, data is the most valuable currency a company possesses.

And when companies provide instant access for their staff to these oil data fields, they enrich themselves by enhancing their ability to make superior business decisions faster from insights derived from the data.

Given this, providing real-time data access for authorized business users, known as self-service analytics, should drive the vision for all organizations and their supporting IT staff. Accomplishing this goal requires the right schematic. Oil was a hot commodity for the better part of a century, but getting to it, transporting it and leveraging its value was by no means a simple task. It required years of research, design and operational sophistication. In other words, a supporting infrastructure was needed to be put into place in order to allow its worth to come to market.
Today, this same scenario can be said for data within corporations. Businesses are scrambling to build a data infrastructure suitable for supporting their desire to mine the riches inherent in their data. And they’re doing so with data estates.

A data estate is simply the infrastructure to help companies systematically manage all of their corporate data. A data estate can be developed on-premises, in the cloud or a combination of both (hybrid). From here, organizations can store, manage and leverage their analytics data, business applications, social data, customer relationship systems, functional business and departmental data, Internet of Things (IoT) and more.
Having a contemporary data estate is even more vital to a corporation when we understand that the growth rate of the amount of raw data coming into a company is astronomical. The good news is that we’re well along the life cycle curve and mature technologies are now available to allow companies to build out their progressive data estate.

Organizations will thrive according to the maturity of their data infrastructure. In a competitive environment where data can make or break a businesses’ competitive advantage, corporate success might very well be measured by the maturity of its enterprise data program. While there are many dimensions to building out a data estate, it’s not as complicated as some might think.

Let’s take a look at three structural columns for designing and implementing your data estate.

First, it starts with a central data hub. It’s important to understand that a central data hub affords companies the possibility of consolidating all corporate data and connecting most data sources and clients. It can digest the volume and velocity of data realms generated by a business and allow for moving and modeling data freely.

A data hub can be the home base for all functionality for managing data such as storage, indexing, cataloging, processing, sourcing, investigation, location, analytics and metadata. Through the data hub, companies can rapidly deploy iterations, new technologies and future requirements to ensure the system is future proof. The hub enables an organization to have a central, single platform that provides integration of all data to ensure “one version of the truth” across the enterprise. And businesses can differentiate user roles for the right type of data access to account for security, safeguards and privacy.

TIP: Capture metadata about the content of your data hub. This should document data sources, record how data is merged, cleansed or altered and provide information for data compliance.

Second, automation can significantly reduce the time it takes to model data. It can simplify and expedite data preparation, loading, and transformation. It can allow you to operationalize the results of your machine learning process as part of your enterprise analytics environment. Automation can remove manual coding and repetitive tasks, replacing it with automatic code generation, which enhance data quality, while freeing up human resources to focus on more strategic initiatives. It can easily track changes and keep systems up-to-date, making maintenance and upgrades much faster and more flexible.

Overall, automation is the linchpin to saving substantial time and costs. Even more, automation can provide for instant and automatic documentation to support data governance and help businesses meet compliance mandates and regulations. With automation, the laborious tasks of documenting, ensuring security, and comprehending data lineage all become a thing of the past.

TIP: Don’t wait for a full business case to start automating. Make a commitment and move towards automating complex, time-consuming, redundant tasks.

Third, consider going to the cloud. Remembering that data is oil, let’s understand that having too much data cannot be viewed as a problem. Instead of looking for ways to reduce data, the vision should be to look for more strategic and economical methods to managing data. And for that reason, the cloud is a viable option.

TIP: Start out small. Develop a lake in the cloud, add a few tools, test, and experiment. Make sure what you design can scale to production.

With the cloud, you only pay for what you use, you eliminate the need for large-scale hardware and software investments, and you can work at anytime from anywhere without disruptive accessibility delays. Deploying analytics data to the cloud can be a time-consuming chore that encompasses many steps, but with automation, organizations can easily populate their analytics data to the cloud, making it quickly available for advanced analytics and artificial intelligence.

So if your company is designing a data estate, it’s important to apply this three-pronged approach. This will help your business seamlessly transform itself into a data-driven organization, helping it strengthen its position in the marketplace and surpass competitors by making quality decisions.

This story originally ran on April 5, 2019 in the Forbes Technology Council.